Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 31103
Generator Capability Curve Constraint for PSO Based Optimal Power Flow

Authors: Mat Syai'in, Adi Soeprijanto, Takashi Hiyama


An optimal power flow (OPF) based on particle swarm optimization (PSO) was developed with more realistic generator security constraint using the capability curve instead of only Pmin/Pmax and Qmin/Qmax. Neural network (NN) was used in designing digital capability curve and the security check algorithm. The algorithm is very simple and flexible especially for representing non linear generation operation limit near steady state stability limit and under excitation operation area. In effort to avoid local optimal power flow solution, the particle swarm optimization was implemented with enough widespread initial population. The objective function used in the optimization process is electric production cost which is dominated by fuel cost. The proposed method was implemented at Java Bali 500 kV power systems contain of 7 generators and 20 buses. The simulation result shows that the combination of generator power output resulted from the proposed method was more economic compared with the result using conventional constraint but operated at more marginal operating point.

Keywords: Neural Network, Particle Swarm Optimization, optimal power flow, Generator Capability Curve

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2193


[1] Sudhakaran, M., Palanivelu,T.G., "GA and PSO culled hybridtechnique for economic dispatch problem with prohibited operating zones", Journal of Zhejiang University, ISSN 1673-565X, pp. 896 - 903, 2007.
[2] Pablo, E., Juan, M.R., "Optimal Power Flow Subject to Security Constraints Solved With a Particle Swarm Optimizer", IEEE Transactions On Power Systems, Vol. 23, No. 1, pp. 33 - 40, 2008.
[3] Gaing, Z.L., Particle swarm optimization to solving the economic dispatch considering the generator constrains, IEEE Trans. On Power System, Vol 18. No. 3, pp. 1187 - 1195, 2003.
[4] Zimmerman,D. Ray, Murilloa E. Carlos, User's Manual A Matlab Power System Simulation Package, Version 3.2 - September 21, PSERC, 2007.
[5] Boukir, T., Labdani, R., "Economic power dispatch of power system with pollution control using multiobjective particle swarm optimization", University of Sharjah Journal of Pure & Applied Sciences, Vol.4. No..2, pp. 57 - 73, 2007.
[6] Wang, C.R., Yuan, H.J., "A modified particle swarm optimization algorithm and its application in optimal power flow problem", Proceedings of the fourth International Conference on machine learning and Cybernetics, Guangzhou, 2005.
[7] Balci, H.H, Valenzuela, J.F., "Scheduling electric power generators using particle swarm optimization combined with the lagrangian relaxation method", AMCS Appl.Math.Comput.Sci, Vol.14. No. 14, pp. 411 - 421, 2004.
[8] Kumari, M.S., Sydulu, M., "An Improved Evolutionary Computation Technique for Optimal Power Flow Solution", International Journal of Innovations in Energy Systems and Power, Vol. 3, no. 1, pp. 32 - 45, 2008.
[9] Younes,M., Rahliga,M., "GA Based Optimal Power Flow Solutions", Electrical & Instrumentation Engineering Department, Thapar University, 2008.
[10] Piccolo, A., Vaccaro, A., "Fuzzy Logic Based Optimal Power Flow Management in Parallel Hybrid Electric Vehicles", Iranian Journal of Electrical and Computer Engineering, Vol. 4, no. 2, pp. 85 - 93, 2005.
[11] Wong,K.P.,Wong,S.Y.W., "Combined Genetic Algorithm/ Simulated Annealing /Fuzzy Set to Short Term Generation Scheduling with Takeor Pay Fuel Contract", IEEE Trans. Power Systems, Vol.11, No.1, pp. 128-136, 1996.
[12] Wong,K.P.,Wong,S.Y.W., "Hybrid Genetic/Simulated Annealing to Short Term Multiple Fuel-Constrained Generation Scheduling", IEEE Trans. Power Systems, Vol.12, No.2, pp. 776-784, 1997.